Overview

Dataset statistics

Number of variables40
Number of observations892
Missing cells1669
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.9 KiB
Average record size in memory320.1 B

Variable types

Categorical21
Numeric19

Alerts

owner has a high cardinality: 238 distinct values High cardinality
farm_name has a high cardinality: 484 distinct values High cardinality
lot_number has a high cardinality: 167 distinct values High cardinality
mill has a high cardinality: 363 distinct values High cardinality
ico_number has a high cardinality: 531 distinct values High cardinality
company has a high cardinality: 217 distinct values High cardinality
altitude has a high cardinality: 308 distinct values High cardinality
region has a high cardinality: 295 distinct values High cardinality
producer has a high cardinality: 544 distinct values High cardinality
grading_date has a high cardinality: 399 distinct values High cardinality
owner1 has a high cardinality: 241 distinct values High cardinality
expiration has a high cardinality: 398 distinct values High cardinality
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
aroma is highly correlated with flavor and 6 other fieldsHigh correlation
flavor is highly correlated with aroma and 6 other fieldsHigh correlation
aftertaste is highly correlated with aroma and 6 other fieldsHigh correlation
acidity is highly correlated with aroma and 6 other fieldsHigh correlation
body is highly correlated with aroma and 6 other fieldsHigh correlation
balance is highly correlated with aroma and 6 other fieldsHigh correlation
uniformity is highly correlated with clean_cupHigh correlation
clean_cup is highly correlated with uniformityHigh correlation
cupper_points is highly correlated with aroma and 6 other fieldsHigh correlation
total_cup_points is highly correlated with aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
aroma is highly correlated with flavor and 6 other fieldsHigh correlation
flavor is highly correlated with aroma and 6 other fieldsHigh correlation
aftertaste is highly correlated with aroma and 6 other fieldsHigh correlation
acidity is highly correlated with aroma and 6 other fieldsHigh correlation
body is highly correlated with aroma and 6 other fieldsHigh correlation
balance is highly correlated with aroma and 6 other fieldsHigh correlation
uniformity is highly correlated with clean_cupHigh correlation
clean_cup is highly correlated with uniformityHigh correlation
cupper_points is highly correlated with aroma and 6 other fieldsHigh correlation
total_cup_points is highly correlated with aroma and 6 other fieldsHigh correlation
altitude_low_meters is highly correlated with altitude_high_meters and 1 other fieldsHigh correlation
altitude_high_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
altitude_mean_meters is highly correlated with altitude_low_meters and 1 other fieldsHigh correlation
aroma is highly correlated with flavor and 5 other fieldsHigh correlation
flavor is highly correlated with aroma and 6 other fieldsHigh correlation
aftertaste is highly correlated with aroma and 6 other fieldsHigh correlation
acidity is highly correlated with aroma and 6 other fieldsHigh correlation
body is highly correlated with flavor and 5 other fieldsHigh correlation
balance is highly correlated with aroma and 6 other fieldsHigh correlation
uniformity is highly correlated with clean_cupHigh correlation
clean_cup is highly correlated with uniformityHigh correlation
cupper_points is highly correlated with aroma and 6 other fieldsHigh correlation
total_cup_points is highly correlated with aroma and 6 other fieldsHigh correlation
unit_of_measurement is highly correlated with in_country_partner and 1 other fieldsHigh correlation
in_country_partner is highly correlated with unit_of_measurement and 1 other fieldsHigh correlation
species is highly correlated with countryof_originHigh correlation
countryof_origin is highly correlated with unit_of_measurement and 2 other fieldsHigh correlation
species is highly correlated with countryof_origin and 1 other fieldsHigh correlation
countryof_origin is highly correlated with species and 18 other fieldsHigh correlation
numberof_bags is highly correlated with countryof_origin and 3 other fieldsHigh correlation
bag_weight is highly correlated with countryof_origin and 8 other fieldsHigh correlation
in_country_partner is highly correlated with countryof_origin and 13 other fieldsHigh correlation
harvest_year is highly correlated with countryof_origin and 10 other fieldsHigh correlation
variety is highly correlated with countryof_origin and 4 other fieldsHigh correlation
processing_method is highly correlated with countryof_origin and 3 other fieldsHigh correlation
moisture is highly correlated with countryof_origin and 3 other fieldsHigh correlation
category_one_defects is highly correlated with countryof_origin and 1 other fieldsHigh correlation
quakers is highly correlated with altitude_low_meters and 2 other fieldsHigh correlation
color is highly correlated with countryof_origin and 1 other fieldsHigh correlation
category_two_defects is highly correlated with category_one_defects and 1 other fieldsHigh correlation
unit_of_measurement is highly correlated with countryof_origin and 2 other fieldsHigh correlation
altitude_low_meters is highly correlated with quakers and 2 other fieldsHigh correlation
altitude_high_meters is highly correlated with quakers and 2 other fieldsHigh correlation
altitude_mean_meters is highly correlated with quakers and 2 other fieldsHigh correlation
aroma is highly correlated with harvest_year and 7 other fieldsHigh correlation
flavor is highly correlated with countryof_origin and 8 other fieldsHigh correlation
aftertaste is highly correlated with countryof_origin and 10 other fieldsHigh correlation
acidity is highly correlated with countryof_origin and 9 other fieldsHigh correlation
body is highly correlated with countryof_origin and 8 other fieldsHigh correlation
balance is highly correlated with countryof_origin and 9 other fieldsHigh correlation
uniformity is highly correlated with aftertaste and 3 other fieldsHigh correlation
clean_cup is highly correlated with aftertaste and 4 other fieldsHigh correlation
sweetness is highly correlated with species and 4 other fieldsHigh correlation
cupper_points is highly correlated with countryof_origin and 10 other fieldsHigh correlation
total_cup_points is highly correlated with countryof_origin and 12 other fieldsHigh correlation
lot_number has 690 (77.4%) missing values Missing
mill has 109 (12.2%) missing values Missing
ico_number has 108 (12.1%) missing values Missing
company has 131 (14.7%) missing values Missing
altitude has 69 (7.7%) missing values Missing
region has 15 (1.7%) missing values Missing
producer has 20 (2.2%) missing values Missing
harvest_year has 19 (2.1%) missing values Missing
variety has 87 (9.8%) missing values Missing
processing_method has 85 (9.5%) missing values Missing
color has 115 (12.9%) missing values Missing
altitude_low_meters has 69 (7.7%) missing values Missing
altitude_high_meters has 69 (7.7%) missing values Missing
altitude_mean_meters has 69 (7.7%) missing values Missing
lot_number is uniformly distributed Uniform
moisture has 137 (15.4%) zeros Zeros
category_one_defects has 797 (89.3%) zeros Zeros
quakers has 826 (92.6%) zeros Zeros
category_two_defects has 244 (27.4%) zeros Zeros

Reproduction

Analysis started2022-02-19 02:10:57.591349
Analysis finished2022-02-19 02:11:51.573051
Duration53.98 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

species
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Arabica
870 
Robusta
 
22

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArabica
2nd rowArabica
3rd rowArabica
4th rowArabica
5th rowArabica

Common Values

ValueCountFrequency (%)
Arabica870
97.5%
Robusta22
 
2.5%

Length

2022-02-19T11:11:51.672226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T11:11:51.738662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
arabica870
97.5%
robusta22
 
2.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

owner
Categorical

HIGH CARDINALITY

Distinct238
Distinct (%)26.9%
Missing7
Missing (%)0.8%
Memory size7.1 KiB
juan luis alvarado romero
154 
ipanema coffees
 
50
cqi taiwan icp cqi台灣合作夥伴
 
37
lin, che-hao krude 林哲豪
 
29
nucoffee
 
24
Other values (233)
591 

Length

Max length47
Median length22
Mean length21.07909605
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique135 ?
Unique (%)15.3%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowgrounds for health admin
4th rowyidnekachew dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
juan luis alvarado romero154
 
17.3%
ipanema coffees50
 
5.6%
cqi taiwan icp cqi台灣合作夥伴37
 
4.1%
lin, che-hao krude 林哲豪29
 
3.3%
nucoffee24
 
2.7%
the coffee source inc.21
 
2.4%
alfredo bojalil16
 
1.8%
cadexsa15
 
1.7%
bismarck castro14
 
1.6%
doi tung development project13
 
1.5%
Other values (228)512
57.4%

Length

2022-02-19T11:11:51.819775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis162
 
5.7%
juan159
 
5.6%
alvarado154
 
5.4%
romero154
 
5.4%
coffee65
 
2.3%
coffees57
 
2.0%
de52
 
1.8%
ipanema50
 
1.8%
cqi42
 
1.5%
s.a40
 
1.4%
Other values (516)1894
66.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

countryof_origin
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Mexico
190 
Guatemala
174 
Brazil
105 
Taiwan
63 
Honduras
50 
Other values (31)
310 

Length

Max length28
Median length7
Mean length8.23206278
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.9%

Sample

1st rowEthiopia
2nd rowEthiopia
3rd rowGuatemala
4th rowEthiopia
5th rowEthiopia

Common Values

ValueCountFrequency (%)
Mexico190
21.3%
Guatemala174
19.5%
Brazil105
11.8%
Taiwan63
 
7.1%
Honduras50
 
5.6%
Costa Rica40
 
4.5%
Ethiopia27
 
3.0%
Tanzania, United Republic Of27
 
3.0%
Uganda23
 
2.6%
Thailand20
 
2.2%
Other values (26)173
19.4%

Length

2022-02-19T11:11:51.930367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mexico190
17.7%
guatemala174
16.2%
brazil105
 
9.8%
taiwan63
 
5.9%
honduras50
 
4.7%
united48
 
4.5%
costa40
 
3.7%
rica40
 
3.7%
ethiopia27
 
2.5%
tanzania27
 
2.5%
Other values (35)308
28.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

farm_name
Categorical

HIGH CARDINALITY

Distinct484
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
various
 
47
rio verde
 
23
several
 
20
finca medina
 
15
doi tung development project
 
13
Other values (479)
774 

Length

Max length73
Median length13
Mean length15.34641256
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique351 ?
Unique (%)39.3%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowsan marcos barrancas "san cristobal cuch
4th rowyidnekachew dabessa coffee plantation
5th rowmetad plc

Common Values

ValueCountFrequency (%)
various47
 
5.3%
rio verde23
 
2.6%
several20
 
2.2%
finca medina15
 
1.7%
doi tung development project13
 
1.5%
fazenda capoeirnha13
 
1.5%
conquista / morito11
 
1.2%
capoeirinha10
 
1.1%
los hicaques10
 
1.1%
el papaturro9
 
1.0%
Other values (474)721
80.8%

Length

2022-02-19T11:11:52.060632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
el95
 
4.4%
finca79
 
3.6%
la77
 
3.5%
coffee67
 
3.1%
various47
 
2.2%
fazenda39
 
1.8%
38
 
1.7%
estate36
 
1.7%
santa35
 
1.6%
verde33
 
1.5%
Other values (747)1635
75.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lot_number
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct167
Distinct (%)82.7%
Missing690
Missing (%)77.4%
Memory size7.1 KiB
1
 
9
020/17
 
6
019/17
 
5
102
 
3
2016 Tainan Coffee Cupping Event Micro Lot 臺南市咖啡評鑑批次
 
3
Other values (162)
176 

Length

Max length71
Median length9
Mean length10.61386139
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)73.3%

Sample

1st row102
2nd rowTsoustructive 2015 Sumatra Typica
3rd row11/23/0252
4th rowBaby Geisha Washed
5th row320

Common Values

ValueCountFrequency (%)
19
 
1.0%
020/176
 
0.7%
019/175
 
0.6%
1023
 
0.3%
2016 Tainan Coffee Cupping Event Micro Lot 臺南市咖啡評鑑批次3
 
0.3%
11/23/05072
 
0.2%
11/52/11702
 
0.2%
22
 
0.2%
472
 
0.2%
2017南投咖啡評鑑 NANTOU COFFEE EVALUATION EVENT 20172
 
0.2%
Other values (157)166
 
18.6%
(Missing)690
77.4%

Length

2022-02-19T11:11:52.201386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
110
 
3.3%
coffee10
 
3.3%
8
 
2.6%
tainan6
 
2.0%
020/176
 
2.0%
evaluation5
 
1.7%
019/175
 
1.7%
event5
 
1.7%
lot5
 
1.7%
20175
 
1.7%
Other values (192)237
78.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mill
Categorical

HIGH CARDINALITY
MISSING

Distinct363
Distinct (%)46.4%
Missing109
Missing (%)12.2%
Memory size7.1 KiB
beneficio ixchel
90 
dry mill
 
38
ipanema coffees
 
16
cadexsa
 
12
beneficio siembras vision (154)
 
12
Other values (358)
615 

Length

Max length77
Median length16
Mean length19.48786718
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243 ?
Unique (%)31.0%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowwolensu
4th rowmetad plc
5th rowc.p.w.e

Common Values

ValueCountFrequency (%)
beneficio ixchel90
 
10.1%
dry mill38
 
4.3%
ipanema coffees16
 
1.8%
cadexsa12
 
1.3%
beneficio siembras vision (154)12
 
1.3%
cigrah s.a de c.v.11
 
1.2%
ipanema comercial e exportadora sa11
 
1.2%
beneficio exportacafe agua santa11
 
1.2%
zaragoza itundujia, oaxaca11
 
1.2%
agroindustrias unidas de mexico8
 
0.9%
Other values (353)563
63.1%
(Missing)109
 
12.2%

Length

2022-02-19T11:11:52.345947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beneficio173
 
7.4%
ixchel93
 
4.0%
de88
 
3.8%
coffee75
 
3.2%
dry45
 
1.9%
mill45
 
1.9%
la36
 
1.5%
el35
 
1.5%
ipanema27
 
1.2%
finca25
 
1.1%
Other values (673)1686
72.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ico_number
Categorical

HIGH CARDINALITY
MISSING

Distinct531
Distinct (%)67.7%
Missing108
Missing (%)12.1%
Memory size7.1 KiB
0
 
63
Taiwan
 
29
2222
 
9
002/4177/0150
 
7
002/1660/0105
 
7
Other values (526)
669 

Length

Max length40
Median length10
Mean length8.985969388
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique438 ?
Unique (%)55.9%

Sample

1st row2014/2015
2nd row2014/2015
3rd row2014/2015
4th row010/0338
5th row010/0338

Common Values

ValueCountFrequency (%)
063
 
7.1%
Taiwan29
 
3.3%
22229
 
1.0%
002/4177/01507
 
0.8%
002/1660/01057
 
0.8%
002/1660/00806
 
0.7%
Taiwan台灣6
 
0.7%
unknown5
 
0.6%
002/1660/00655
 
0.6%
11-951-1365
 
0.6%
Other values (521)642
72.0%
(Missing)108
 
12.1%

Length

2022-02-19T11:11:52.487167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
063
 
7.5%
taiwan29
 
3.5%
11
 
1.3%
22229
 
1.1%
none8
 
1.0%
002/4177/01507
 
0.8%
002/1660/01057
 
0.8%
unspecified7
 
0.8%
hdoa6
 
0.7%
002/1660/00806
 
0.7%
Other values (548)682
81.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

company
Categorical

HIGH CARDINALITY
MISSING

Distinct217
Distinct (%)28.5%
Missing131
Missing (%)14.7%
Memory size7.1 KiB
unex guatemala, s.a.
86 
ipanema coffees
 
50
blossom valley宸嶧國際
 
21
nucoffee
 
20
taiwan coffee laboratory
 
20
Other values (212)
564 

Length

Max length78
Median length20
Mean length20.49671485
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)15.0%

Sample

1st rowmetad agricultural developmet plc
2nd rowmetad agricultural developmet plc
3rd rowyidnekachew debessa coffee plantation
4th rowmetad agricultural developmet plc
5th rowdiamond enterprise plc

Common Values

ValueCountFrequency (%)
unex guatemala, s.a.86
 
9.6%
ipanema coffees50
 
5.6%
blossom valley宸嶧國際21
 
2.4%
nucoffee20
 
2.2%
taiwan coffee laboratory20
 
2.2%
the coffee source inc.17
 
1.9%
ecomtrading16
 
1.8%
cadexsa15
 
1.7%
siembras vision, s.a.14
 
1.6%
宸嶧國際14
 
1.6%
Other values (207)488
54.7%
(Missing)131
 
14.7%

Length

2022-02-19T11:11:52.631435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.a200
 
8.4%
de141
 
5.9%
coffee136
 
5.7%
guatemala104
 
4.4%
unex86
 
3.6%
coffees58
 
2.4%
ltd50
 
2.1%
ipanema50
 
2.1%
c.v37
 
1.6%
exportadora36
 
1.5%
Other values (414)1476
62.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

altitude
Categorical

HIGH CARDINALITY
MISSING

Distinct308
Distinct (%)37.4%
Missing69
Missing (%)7.7%
Memory size7.1 KiB
1100
 
36
1200
 
35
4300
 
31
1400
 
28
1250
 
28
Other values (303)
665 

Length

Max length41
Median length4
Mean length5.77764277
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195 ?
Unique (%)23.7%

Sample

1st row1950-2200
2nd row1950-2200
3rd row1600 - 1800 m
4th row1800-2200
5th row1950-2200

Common Values

ValueCountFrequency (%)
110036
 
4.0%
120035
 
3.9%
430031
 
3.5%
140028
 
3.1%
125028
 
3.1%
130027
 
3.0%
150026
 
2.9%
170021
 
2.4%
135017
 
1.9%
100013
 
1.5%
Other values (298)561
62.9%
(Missing)69
 
7.7%

Length

2022-02-19T11:11:52.767537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
120050
 
4.7%
140046
 
4.3%
m44
 
4.1%
130039
 
3.6%
110038
 
3.5%
150037
 
3.4%
430031
 
2.9%
125029
 
2.7%
msnm27
 
2.5%
400026
 
2.4%
Other values (263)706
65.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

region
Categorical

HIGH CARDINALITY
MISSING

Distinct295
Distinct (%)33.6%
Missing15
Missing (%)1.7%
Memory size7.1 KiB
oriente
79 
south of minas
 
65
veracruz
 
30
comayagua
 
16
san marcos
 
16
Other values (290)
671 

Length

Max length76
Median length10
Mean length12.30900798
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)19.0%

Sample

1st rowguji-hambela
2nd rowguji-hambela
3rd roworomia
4th rowguji-hambela
5th roworomia

Common Values

ValueCountFrequency (%)
oriente79
 
8.9%
south of minas65
 
7.3%
veracruz30
 
3.4%
comayagua16
 
1.8%
san marcos16
 
1.8%
tarrazu16
 
1.8%
marcala15
 
1.7%
huehuetenango15
 
1.7%
antigua15
 
1.7%
jinotega12
 
1.3%
Other values (285)598
67.0%
(Missing)15
 
1.7%

Length

2022-02-19T11:11:52.906108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oriente88
 
5.4%
minas84
 
5.1%
of70
 
4.3%
south69
 
4.2%
san36
 
2.2%
de35
 
2.1%
chiapas32
 
2.0%
veracruz31
 
1.9%
marcos21
 
1.3%
la21
 
1.3%
Other values (421)1145
70.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

producer
Categorical

HIGH CARDINALITY
MISSING

Distinct544
Distinct (%)62.4%
Missing20
Missing (%)2.2%
Memory size7.1 KiB
Ipanema Agrícola SA
 
22
VARIOS
 
12
Ipanema Agricola
 
12
Ipanema Agricola S.A
 
11
ROBERTO MONTERROSO
 
10
Other values (539)
805 

Length

Max length100
Median length19
Mean length20.46100917
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)47.5%

Sample

1st rowMETAD PLC
2nd rowMETAD PLC
3rd rowYidnekachew Dabessa Coffee Plantation
4th rowMETAD PLC
5th rowBazen Agricultural & Industrial Dev't Plc

Common Values

ValueCountFrequency (%)
Ipanema Agrícola SA22
 
2.5%
VARIOS12
 
1.3%
Ipanema Agricola12
 
1.3%
Ipanema Agricola S.A11
 
1.2%
ROBERTO MONTERROSO10
 
1.1%
AMILCAR LAPOLA9
 
1.0%
Doi Tung Development Project9
 
1.0%
AGROPECUARIA QUIAGRAL8
 
0.9%
Reinerio Zepeda8
 
0.9%
Martin Gutierrez7
 
0.8%
Other values (534)764
85.7%
(Missing)20
 
2.2%

Length

2022-02-19T11:11:53.056472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de66
 
2.4%
ipanema50
 
1.8%
coffee49
 
1.8%
s.a48
 
1.8%
agricola37
 
1.4%
35
 
1.3%
sa28
 
1.0%
ltd25
 
0.9%
varios24
 
0.9%
jose23
 
0.8%
Other values (1031)2338
85.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

numberof_bags
Real number (ℝ≥0)

HIGH CORRELATION

Distinct98
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.5560538
Minimum1
Maximum1062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:53.196760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q115
median150
Q3275
95-th percentile320
Maximum1062
Range1061
Interquartile range (IQR)260

Descriptive statistics

Standard deviation132.1337545
Coefficient of variation (CV)0.8718474197
Kurtosis0.9086227062
Mean151.5560538
Median Absolute Deviation (MAD)125
Skewness0.4820286157
Sum135188
Variance17459.32907
MonotonicityNot monotonic
2022-02-19T11:11:53.325634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250148
16.6%
275123
13.8%
1084
 
9.4%
32058
 
6.5%
158
 
6.5%
30050
 
5.6%
5033
 
3.7%
2030
 
3.4%
10026
 
2.9%
221
 
2.4%
Other values (88)261
29.3%
ValueCountFrequency (%)
158
6.5%
221
 
2.4%
311
 
1.2%
43
 
0.3%
57
 
0.8%
62
 
0.2%
72
 
0.2%
86
 
0.7%
1084
9.4%
114
 
0.4%
ValueCountFrequency (%)
10621
 
0.1%
5502
 
0.2%
5002
 
0.2%
4502
 
0.2%
4401
 
0.1%
4001
 
0.1%
3801
 
0.1%
3771
 
0.1%
3602
 
0.2%
32058
6.5%

bag_weight
Categorical

HIGH CORRELATION

Distinct42
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
1 kg
265 
69 kg
192 
60 kg
181 
2 kg
99 
30 kg
 
23
Other values (37)
132 

Length

Max length8
Median length5
Mean length4.548206278
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)1.5%

Sample

1st row60 kg
2nd row60 kg
3rd row1
4th row60 kg
5th row60 kg

Common Values

ValueCountFrequency (%)
1 kg265
29.7%
69 kg192
21.5%
60 kg181
20.3%
2 kg99
 
11.1%
30 kg23
 
2.6%
50 kg13
 
1.5%
612
 
1.3%
70 kg10
 
1.1%
20 kg10
 
1.1%
10 kg9
 
1.0%
Other values (32)78
 
8.7%

Length

2022-02-19T11:11:53.447052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kg845
47.8%
1275
 
15.6%
69192
 
10.9%
60181
 
10.2%
2101
 
5.7%
lbs31
 
1.8%
3023
 
1.3%
614
 
0.8%
5013
 
0.7%
511
 
0.6%
Other values (26)82
 
4.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

in_country_partner
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Specialty Coffee Association
183 
AMECAFE
165 
Asociacion Nacional Del Café
154 
Instituto Hondureño del Café
59 
Brazil Specialty Coffee Association
51 
Other values (21)
280 

Length

Max length62
Median length28
Mean length24.86995516
Min length7

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.3%

Sample

1st rowMETAD Agricultural Development plc
2nd rowMETAD Agricultural Development plc
3rd rowSpecialty Coffee Association
4th rowMETAD Agricultural Development plc
5th rowMETAD Agricultural Development plc

Common Values

ValueCountFrequency (%)
Specialty Coffee Association183
20.5%
AMECAFE165
18.5%
Asociacion Nacional Del Café154
17.3%
Instituto Hondureño del Café59
 
6.6%
Brazil Specialty Coffee Association51
 
5.7%
Blossom Valley International46
 
5.2%
Specialty Coffee Association of Costa Rica37
 
4.1%
Africa Fine Coffee Association33
 
3.7%
NUCOFFEE28
 
3.1%
Uganda Coffee Development Authority23
 
2.6%
Other values (16)113
12.7%

Length

2022-02-19T11:11:53.555898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coffee382
13.6%
association323
11.5%
specialty286
 
10.2%
del220
 
7.8%
café220
 
7.8%
amecafe165
 
5.9%
asociacion154
 
5.5%
nacional154
 
5.5%
instituto59
 
2.1%
hondureño59
 
2.1%
Other values (46)786
28.0%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

harvest_year
Categorical

HIGH CORRELATION
MISSING

Distinct28
Distinct (%)3.2%
Missing19
Missing (%)2.1%
Memory size7.1 KiB
2012
233 
2014
160 
2013
104 
2015
98 
2016
83 
Other values (23)
195 

Length

Max length24
Median length4
Mean length4.720504009
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)1.1%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th rowMarch 2010

Common Values

ValueCountFrequency (%)
2012233
26.1%
2014160
17.9%
2013104
11.7%
201598
11.0%
201683
 
9.3%
201759
 
6.6%
2013/201425
 
2.8%
2015/201621
 
2.4%
201120
 
2.2%
2014/201515
 
1.7%
Other values (18)55
 
6.2%
(Missing)19
 
2.1%

Length

2022-02-19T11:11:53.674709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012233
25.2%
2014160
17.3%
2013104
11.2%
201598
10.6%
201686
 
9.3%
201775
 
8.1%
2013/201425
 
2.7%
2015/201621
 
2.3%
201121
 
2.3%
18
 
1.9%
Other values (24)85
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

grading_date
Categorical

HIGH CARDINALITY

Distinct399
Distinct (%)44.7%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
July 11th, 2012
 
22
December 26th, 2013
 
20
June 6th, 2012
 
15
July 26th, 2012
 
13
October 8th, 2015
 
13
Other values (394)
809 

Length

Max length20
Median length16
Mean length16.40246637
Min length13

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)25.3%

Sample

1st rowApril 4th, 2015
2nd rowApril 4th, 2015
3rd rowMay 31st, 2010
4th rowMarch 26th, 2015
5th rowApril 4th, 2015

Common Values

ValueCountFrequency (%)
July 11th, 201222
 
2.5%
December 26th, 201320
 
2.2%
June 6th, 201215
 
1.7%
July 26th, 201213
 
1.5%
October 8th, 201513
 
1.5%
March 29th, 201312
 
1.3%
September 27th, 201211
 
1.2%
October 20th, 201711
 
1.2%
August 16th, 20169
 
1.0%
June 1st, 20179
 
1.0%
Other values (389)757
84.9%

Length

2022-02-19T11:11:53.793404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012218
 
8.1%
2014172
 
6.4%
2015142
 
5.3%
june115
 
4.3%
2017109
 
4.1%
may104
 
3.9%
july103
 
3.8%
201691
 
3.4%
201390
 
3.4%
april89
 
3.3%
Other values (42)1443
53.9%

Most occurring characters

ValueCountFrequency (%)
2
100.0%

Most occurring categories

ValueCountFrequency (%)
Control2
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
100.0%

owner1
Categorical

HIGH CARDINALITY

Distinct241
Distinct (%)27.2%
Missing7
Missing (%)0.8%
Memory size7.1 KiB
Juan Luis Alvarado Romero
154 
Ipanema Coffees
 
50
CQI Taiwan ICP CQI台灣合作夥伴
 
36
Lin, Che-Hao Krude 林哲豪
 
28
NUCOFFEE
 
24
Other values (236)
593 

Length

Max length47
Median length22
Mean length21.0779661
Min length3

Characters and Unicode

Total characters2
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique138 ?
Unique (%)15.6%

Sample

1st rowmetad plc
2nd rowmetad plc
3rd rowGrounds for Health Admin
4th rowYidnekachew Dabessa
5th rowmetad plc

Common Values

ValueCountFrequency (%)
Juan Luis Alvarado Romero154
 
17.3%
Ipanema Coffees50
 
5.6%
CQI Taiwan ICP CQI台灣合作夥伴36
 
4.0%
Lin, Che-Hao Krude 林哲豪28
 
3.1%
NUCOFFEE24
 
2.7%
The Coffee Source Inc.21
 
2.4%
ALFREDO BOJALIL16
 
1.8%
CADEXSA15
 
1.7%
Bismarck Castro14
 
1.6%
Doi Tung Development Project13
 
1.5%
Other values (231)514
57.6%

Length

2022-02-19T11:11:53.918231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luis162
 
5.7%
juan159
 
5.6%
alvarado154
 
5.4%
romero154
 
5.4%
coffee65
 
2.3%
coffees57
 
2.0%
de52
 
1.8%
ipanema50
 
1.8%
cqi42
 
1.5%
s.a39
 
1.4%
Other values (516)1892
66.9%

Most occurring characters

ValueCountFrequency (%)
2
100.0%

Most occurring categories

ValueCountFrequency (%)
Control2
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2
100.0%

variety
Categorical

HIGH CORRELATION
MISSING

Distinct26
Distinct (%)3.2%
Missing87
Missing (%)9.8%
Memory size7.1 KiB
Bourbon
217 
Typica
169 
Caturra
124 
Other
74 
Catuai
70 
Other values (21)
151 

Length

Max length21
Median length7
Mean length6.850931677
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.6%

Sample

1st rowOther
2nd rowBourbon
3rd rowOther
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Bourbon217
24.3%
Typica169
18.9%
Caturra124
13.9%
Other74
 
8.3%
Catuai70
 
7.8%
Yellow Bourbon34
 
3.8%
Mundo Novo22
 
2.5%
Catimor16
 
1.8%
Pacas13
 
1.5%
SL1413
 
1.5%
Other values (16)53
 
5.9%
(Missing)87
9.8%

Length

2022-02-19T11:11:54.034535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bourbon251
28.9%
typica169
19.4%
caturra124
14.3%
other74
 
8.5%
catuai70
 
8.1%
yellow34
 
3.9%
mundo22
 
2.5%
novo22
 
2.5%
catimor16
 
1.8%
pacas13
 
1.5%
Other values (20)74
 
8.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

processing_method
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.6%
Missing85
Missing (%)9.5%
Memory size7.1 KiB
Washed / Wet
579 
Natural / Dry
149 
Semi-washed / Semi-pulped
 
44
Other
 
23
Pulped natural / honey
 
12

Length

Max length25
Median length12
Mean length12.84262701
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWashed / Wet
2nd rowWashed / Wet
3rd rowNatural / Dry
4th rowWashed / Wet
5th rowNatural / Dry

Common Values

ValueCountFrequency (%)
Washed / Wet579
64.9%
Natural / Dry149
 
16.7%
Semi-washed / Semi-pulped44
 
4.9%
Other23
 
2.6%
Pulped natural / honey12
 
1.3%
(Missing)85
 
9.5%

Length

2022-02-19T11:11:54.149005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T11:11:54.227492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
784
32.8%
washed579
24.3%
wet579
24.3%
natural161
 
6.7%
dry149
 
6.2%
semi-washed44
 
1.8%
semi-pulped44
 
1.8%
other23
 
1.0%
pulped12
 
0.5%
honey12
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

moisture
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09428251121
Minimum0
Maximum0.28
Zeros137
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:54.308376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.11
Q30.12
95-th percentile0.13
Maximum0.28
Range0.28
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.04462892027
Coefficient of variation (CV)0.4733531139
Kurtosis0.8536969419
Mean0.09428251121
Median Absolute Deviation (MAD)0.01
Skewness-1.255459328
Sum84.1
Variance0.001991740524
MonotonicityNot monotonic
2022-02-19T11:11:54.398828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.11258
28.9%
0.12208
23.3%
0.1141
15.8%
0137
15.4%
0.1355
 
6.2%
0.0922
 
2.5%
0.1421
 
2.4%
0.018
 
0.9%
0.157
 
0.8%
0.086
 
0.7%
Other values (11)29
 
3.3%
ValueCountFrequency (%)
0137
15.4%
0.018
 
0.9%
0.024
 
0.4%
0.032
 
0.2%
0.042
 
0.2%
0.053
 
0.3%
0.062
 
0.2%
0.074
 
0.4%
0.086
 
0.7%
0.0922
 
2.5%
ValueCountFrequency (%)
0.281
 
0.1%
0.23
 
0.3%
0.181
 
0.1%
0.173
 
0.3%
0.164
 
0.4%
0.157
 
0.8%
0.1421
 
2.4%
0.1355
 
6.2%
0.12208
23.3%
0.11258
28.9%

category_one_defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3139013453
Minimum0
Maximum31
Zeros797
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:54.491540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum31
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.791020006
Coefficient of variation (CV)5.705678021
Kurtosis200.5334283
Mean0.3139013453
Median Absolute Deviation (MAD)0
Skewness12.76662765
Sum280
Variance3.207752664
MonotonicityNot monotonic
2022-02-19T11:11:54.579866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0797
89.3%
147
 
5.3%
222
 
2.5%
39
 
1.0%
54
 
0.4%
44
 
0.4%
312
 
0.2%
72
 
0.2%
101
 
0.1%
111
 
0.1%
Other values (3)3
 
0.3%
ValueCountFrequency (%)
0797
89.3%
147
 
5.3%
222
 
2.5%
39
 
1.0%
44
 
0.4%
54
 
0.4%
61
 
0.1%
72
 
0.2%
81
 
0.1%
101
 
0.1%
ValueCountFrequency (%)
312
 
0.2%
151
 
0.1%
111
 
0.1%
101
 
0.1%
81
 
0.1%
72
 
0.2%
61
 
0.1%
54
0.4%
44
0.4%
39
1.0%

quakers
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1603139013
Minimum0
Maximum8
Zeros826
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:54.670147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7216976433
Coefficient of variation (CV)4.501778306
Kurtosis47.68993688
Mean0.1603139013
Median Absolute Deviation (MAD)0
Skewness6.338467266
Sum143
Variance0.5208474883
MonotonicityNot monotonic
2022-02-19T11:11:54.766665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0826
92.6%
129
 
3.3%
224
 
2.7%
43
 
0.3%
53
 
0.3%
63
 
0.3%
32
 
0.2%
71
 
0.1%
81
 
0.1%
ValueCountFrequency (%)
0826
92.6%
129
 
3.3%
224
 
2.7%
32
 
0.2%
43
 
0.3%
53
 
0.3%
63
 
0.3%
71
 
0.1%
81
 
0.1%
ValueCountFrequency (%)
81
 
0.1%
71
 
0.1%
63
 
0.3%
53
 
0.3%
43
 
0.3%
32
 
0.2%
224
 
2.7%
129
 
3.3%
0826
92.6%

color
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)0.5%
Missing115
Missing (%)12.9%
Memory size7.1 KiB
Green
611 
Bluish-Green
70 
Blue-Green
 
57
None
 
39

Length

Max length12
Median length5
Mean length5.947232947
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreen
2nd rowGreen
3rd rowGreen
4th rowGreen
5th rowGreen

Common Values

ValueCountFrequency (%)
Green611
68.5%
Bluish-Green70
 
7.8%
Blue-Green57
 
6.4%
None39
 
4.4%
(Missing)115
 
12.9%

Length

2022-02-19T11:11:54.877276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T11:11:54.951196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
green611
78.6%
bluish-green70
 
9.0%
blue-green57
 
7.3%
none39
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

category_two_defects
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.412556054
Minimum0
Maximum47
Zeros244
Zeros (%)27.4%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:55.028850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile12
Maximum47
Range47
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.749880898
Coefficient of variation (CV)1.391883627
Kurtosis18.41695823
Mean3.412556054
Median Absolute Deviation (MAD)2
Skewness3.461377641
Sum3044
Variance22.56136854
MonotonicityNot monotonic
2022-02-19T11:11:55.142945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0244
27.4%
1129
14.5%
2124
13.9%
394
 
10.5%
483
 
9.3%
551
 
5.7%
632
 
3.6%
730
 
3.4%
822
 
2.5%
916
 
1.8%
Other values (20)67
 
7.5%
ValueCountFrequency (%)
0244
27.4%
1129
14.5%
2124
13.9%
394
 
10.5%
483
 
9.3%
551
 
5.7%
632
 
3.6%
730
 
3.4%
822
 
2.5%
916
 
1.8%
ValueCountFrequency (%)
471
0.1%
381
0.1%
341
0.1%
321
0.1%
302
0.2%
281
0.1%
261
0.1%
232
0.2%
221
0.1%
211
0.1%

expiration
Categorical

HIGH CARDINALITY

Distinct398
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
July 11th, 2013
 
22
December 26th, 2014
 
21
June 6th, 2013
 
15
July 26th, 2013
 
13
October 7th, 2016
 
13
Other values (393)
808 

Length

Max length20
Median length16
Mean length16.39461883
Min length13

Characters and Unicode

Total characters1
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)25.2%

Sample

1st rowApril 3rd, 2016
2nd rowApril 3rd, 2016
3rd rowMay 31st, 2011
4th rowMarch 25th, 2016
5th rowApril 3rd, 2016

Common Values

ValueCountFrequency (%)
July 11th, 201322
 
2.5%
December 26th, 201421
 
2.4%
June 6th, 201315
 
1.7%
July 26th, 201313
 
1.5%
October 7th, 201613
 
1.5%
March 29th, 201412
 
1.3%
October 20th, 201811
 
1.2%
September 27th, 201311
 
1.2%
September 11th, 20139
 
1.0%
June 17th, 20119
 
1.0%
Other values (388)756
84.8%

Length

2022-02-19T11:11:55.254396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013218
 
8.1%
2015172
 
6.4%
2016142
 
5.3%
june115
 
4.3%
2018109
 
4.1%
may104
 
3.9%
july103
 
3.8%
201791
 
3.4%
201490
 
3.4%
april89
 
3.3%
Other values (42)1443
53.9%

Most occurring characters

ValueCountFrequency (%)
1
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1
100.0%

unit_of_measurement
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
m
778 
ft
114 

Length

Max length2
Median length1
Mean length1.127802691
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m778
87.2%
ft114
 
12.8%

Length

2022-02-19T11:11:55.363456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-19T11:11:55.430497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
m778
87.2%
ft114
 
12.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

altitude_low_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct169
Distinct (%)20.5%
Missing69
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1871.956144
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:55.508853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile428.148
Q11100
median1300
Q31500
95-th percentile1854.5
Maximum190164
Range190163
Interquartile range (IQR)400

Descriptive statistics

Standard deviation10058.70195
Coefficient of variation (CV)5.373364103
Kurtosis314.2995633
Mean1871.956144
Median Absolute Deviation (MAD)200
Skewness17.5139798
Sum1540619.906
Variance101177484.8
MonotonicityNot monotonic
2022-02-19T11:11:55.639758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120067
 
7.5%
140049
 
5.5%
150047
 
5.3%
130043
 
4.8%
110043
 
4.8%
125035
 
3.9%
1310.6431
 
3.5%
100029
 
3.3%
1219.228
 
3.1%
145024
 
2.7%
Other values (159)427
47.9%
(Missing)69
 
7.7%
ValueCountFrequency (%)
111
1.2%
123
 
0.3%
132
 
0.2%
401
 
0.1%
1001
 
0.1%
1251
 
0.1%
1502
 
0.2%
157.88643
 
0.3%
1601
 
0.1%
1681
 
0.1%
ValueCountFrequency (%)
1901642
0.2%
1100001
 
0.1%
110001
 
0.1%
42871
 
0.1%
40011
 
0.1%
38451
 
0.1%
38251
 
0.1%
38001
 
0.1%
35001
 
0.1%
32803
0.3%

altitude_high_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct165
Distinct (%)20.0%
Missing69
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1906.597621
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:55.778365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile532.1
Q11100
median1300
Q31500
95-th percentile1900
Maximum190164
Range190163
Interquartile range (IQR)400

Descriptive statistics

Standard deviation10058.28152
Coefficient of variation (CV)5.275513513
Kurtosis314.1102333
Mean1906.597621
Median Absolute Deviation (MAD)200
Skewness17.50592704
Sum1569129.842
Variance101169027
MonotonicityNot monotonic
2022-02-19T11:11:56.405248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140056
 
6.3%
120054
 
6.1%
150043
 
4.8%
110043
 
4.8%
130039
 
4.4%
125037
 
4.1%
1310.6431
 
3.5%
100030
 
3.4%
170029
 
3.3%
1219.228
 
3.1%
Other values (155)433
48.5%
(Missing)69
 
7.7%
ValueCountFrequency (%)
111
1.2%
123
 
0.3%
132
 
0.2%
401
 
0.1%
1001
 
0.1%
1251
 
0.1%
1502
 
0.2%
157.88643
 
0.3%
1681
 
0.1%
1701
 
0.1%
ValueCountFrequency (%)
1901642
0.2%
1100001
0.1%
110001
0.1%
59001
0.1%
42871
0.1%
40011
0.1%
38451
0.1%
38251
0.1%
38001
0.1%
35001
0.1%

altitude_mean_meters
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct178
Distinct (%)21.6%
Missing69
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean1889.276883
Minimum1
Maximum190164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:56.544562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile525.7
Q11100
median1300
Q31500
95-th percentile1879.2
Maximum190164
Range190163
Interquartile range (IQR)400

Descriptive statistics

Standard deviation10058.09841
Coefficient of variation (CV)5.323782078
Kurtosis314.2544971
Mean1889.276883
Median Absolute Deviation (MAD)200
Skewness17.51198274
Sum1554874.874
Variance101165343.6
MonotonicityNot monotonic
2022-02-19T11:11:56.680762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120056
 
6.3%
140046
 
5.2%
130045
 
5.0%
110041
 
4.6%
150038
 
4.3%
125037
 
4.1%
100031
 
3.5%
1310.6431
 
3.5%
1219.228
 
3.1%
170024
 
2.7%
Other values (168)446
50.0%
(Missing)69
 
7.7%
ValueCountFrequency (%)
111
1.2%
123
 
0.3%
132
 
0.2%
401
 
0.1%
1001
 
0.1%
1251
 
0.1%
1502
 
0.2%
157.88643
 
0.3%
1681
 
0.1%
1701
 
0.1%
ValueCountFrequency (%)
1901642
0.2%
1100001
0.1%
110001
0.1%
42871
0.1%
40011
0.1%
38501
0.1%
38451
0.1%
38251
0.1%
38001
0.1%
35001
0.1%

aroma
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.98099118
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:56.808562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.33209284
Q144.79177209
median50.88048919
Q356.73472462
95-th percentile65.82315007
Maximum100
Range100
Interquartile range (IQR)11.94295253

Descriptive statistics

Standard deviation9.766604472
Coefficient of variation (CV)0.1954063783
Kurtosis1.118013919
Mean49.98099118
Median Absolute Deviation (MAD)6.088717097
Skewness-0.01152027063
Sum44583.04413
Variance95.38656292
MonotonicityNot monotonic
2022-02-19T11:11:56.928973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
47.83094141124
13.9%
53.78866215108
12.1%
50.88048919100
11.2%
44.7917720985
9.5%
56.7347246279
8.9%
42.1656789168
7.6%
59.4556764659
6.6%
39.5262120757
 
6.4%
63.792143136
 
4.0%
61.6252617830
 
3.4%
Other values (20)146
16.4%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
22.673824941
 
0.1%
24.437299592
 
0.2%
26.2359363
 
0.3%
27.86071954
 
0.4%
29.574308457
 
0.8%
31.3729713510
1.1%
33.3320928418
2.0%
35.3611035421
2.4%
ValueCountFrequency (%)
1001
 
0.1%
77.326175052
 
0.2%
74.737306153
 
0.3%
72.541048345
 
0.6%
70.691525246
 
0.7%
69.089525498
 
0.9%
67.3117999114
 
1.6%
65.8231500711
 
1.2%
63.792143136
4.0%
61.6252617830
3.4%

flavor
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.98010169
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:57.047006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.38268431
Q143.90740166
median48.99718117
Q357.33580623
95-th percentile64.46883242
Maximum100
Range100
Interquartile range (IQR)13.42840457

Descriptive statistics

Standard deviation9.769409756
Coefficient of variation (CV)0.195465984
Kurtosis1.104518214
Mean49.98010169
Median Absolute Deviation (MAD)5.762348091
Skewness-0.009760782346
Sum44582.25071
Variance95.44136698
MonotonicityNot monotonic
2022-02-19T11:11:57.163174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
48.99718117123
13.8%
52.07203829103
11.5%
54.7595292683
9.3%
43.9074016683
9.3%
57.3358062372
 
8.1%
46.2843227571
 
8.0%
59.8817294655
 
6.2%
41.5261480250
 
5.6%
39.5750343739
 
4.4%
62.2385709933
 
3.7%
Other values (22)180
20.2%
ValueCountFrequency (%)
01
 
0.1%
21.803334132
 
0.2%
24.873097063
 
0.3%
28.04794389
 
1.0%
29.70158043
 
0.3%
30.610253454
 
0.4%
31.631043747
 
0.8%
33.2092406515
1.7%
34.382684317
 
0.8%
36.0933862231
3.5%
ValueCountFrequency (%)
1001
 
0.1%
76.058317724
 
0.4%
73.7640642
 
0.2%
72.541048344
 
0.4%
70.97417944
 
0.4%
69.936565834
 
0.4%
69.187532263
 
0.3%
67.8892596512
 
1.3%
66.4885778910
 
1.1%
64.4688324230
3.4%

aftertaste
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.81679103
Minimum0
Maximum100
Zeros7
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:57.283405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.36110354
Q143.02108155
median50.90766055
Q356.2087789
95-th percentile65.32136347
Maximum100
Range100
Interquartile range (IQR)13.18769734

Descriptive statistics

Standard deviation10.38830436
Coefficient of variation (CV)0.2085301792
Kurtosis3.543349748
Mean49.81679103
Median Absolute Deviation (MAD)5.439167929
Skewness-0.6072754651
Sum44436.5776
Variance107.9168675
MonotonicityNot monotonic
2022-02-19T11:11:57.400595image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
48.20403928109
12.2%
53.57034226101
11.3%
50.9076605590
10.1%
45.4684926282
9.2%
43.0210815569
 
7.7%
56.208778968
 
7.6%
58.4738519856
 
6.3%
60.7979485248
 
5.4%
39.2779404843
 
4.8%
63.3527155938
 
4.3%
Other values (22)188
21.1%
ValueCountFrequency (%)
07
 
0.8%
26.770869851
 
0.1%
28.227199636
 
0.7%
29.574308453
 
0.3%
30.610253456
 
0.7%
31.372971353
 
0.3%
32.4790072110
 
1.1%
33.627862928
 
0.9%
35.3611035428
3.1%
37.3231882725
2.8%
ValueCountFrequency (%)
1001
 
0.1%
77.326175052
 
0.2%
74.384518424
 
0.4%
72.986497832
 
0.2%
72.335292671
 
0.1%
71.600804913
 
0.3%
70.174606756
 
0.7%
68.993462255
 
0.6%
67.0450261820
2.2%
65.3213634711
1.2%

acidity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct28
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.98701945
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:57.518835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.20924065
Q143.65732305
median49.06516085
Q357.53645609
95-th percentile66.25804829
Maximum100
Range100
Interquartile range (IQR)13.87913303

Descriptive statistics

Standard deviation9.770744375
Coefficient of variation (CV)0.1954656325
Kurtosis1.116504553
Mean49.98701945
Median Absolute Deviation (MAD)5.663813263
Skewness-0.004191848072
Sum44588.42135
Variance95.46744564
MonotonicityNot monotonic
2022-02-19T11:11:57.636562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
49.06516085115
12.9%
54.7289741199
11.1%
51.9338002796
10.8%
46.167442291
10.2%
43.6573230570
7.8%
57.5364560969
7.7%
41.3045104958
 
6.5%
38.7049258855
 
6.2%
59.9046921448
 
5.4%
61.9697547932
 
3.6%
Other values (18)159
17.8%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
22.673824941
 
0.1%
24.873097063
 
0.3%
26.512482242
 
0.2%
28.399195097
 
0.8%
30.399083668
 
0.9%
33.2092406527
3.0%
35.9376243930
3.4%
38.7049258855
6.2%
ValueCountFrequency (%)
1001
 
0.1%
79.386359161
 
0.1%
74.737306155
 
0.6%
72.13928056
 
0.7%
70.174606756
 
0.7%
68.899257726
 
0.7%
67.73882128
 
0.9%
66.2580482916
1.8%
64.0623756130
3.4%
61.9697547932
3.6%

body
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.98226537
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:57.753801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.62786292
Q144.28308103
median50.31117725
Q355.91180053
95-th percentile65.92884501
Maximum100
Range100
Interquartile range (IQR)11.6287195

Descriptive statistics

Standard deviation9.760727906
Coefficient of variation (CV)0.1952838238
Kurtosis1.119471043
Mean49.98226537
Median Absolute Deviation (MAD)6.028096222
Skewness-0.009035603902
Sum44584.18071
Variance95.27180926
MonotonicityNot monotonic
2022-02-19T11:11:57.865809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
50.31117725140
15.7%
44.28308103100
11.2%
47.1449744191
10.2%
55.9118005389
10.0%
53.3394758678
8.7%
58.6954895166
7.4%
41.4460785360
6.7%
38.7854187255
 
6.2%
61.4584793952
 
5.8%
33.6278629231
 
3.5%
Other values (20)130
14.6%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
23.365366252
 
0.2%
24.873097061
 
0.1%
25.615481581
 
0.1%
26.512482242
 
0.2%
27.86071954
 
0.4%
29.02582063
 
0.3%
30.6102534510
 
1.1%
33.6278629231
3.5%
ValueCountFrequency (%)
1001
 
0.1%
77.326175052
 
0.2%
75.126902942
 
0.2%
73.229130154
 
0.4%
71.772800374
 
0.4%
70.425691554
 
0.4%
68.4535099815
 
1.7%
65.9288450124
2.7%
63.8300824722
2.5%
61.4584793952
5.8%

balance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.98188839
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:57.983801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.43304051
Q143.02108155
median50.36531914
Q355.58829507
95-th percentile64.68213542
Maximum100
Range100
Interquartile range (IQR)12.56721352

Descriptive statistics

Standard deviation9.775678817
Coefficient of variation (CV)0.1955844233
Kurtosis1.09817778
Mean49.98188839
Median Absolute Deviation (MAD)5.232173475
Skewness-0.01269023919
Sum44583.84444
Variance95.56389633
MonotonicityNot monotonic
2022-02-19T11:11:58.106344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
50.36531914132
14.8%
55.5882950785
9.5%
47.4405364482
9.2%
45.1331456777
8.6%
53.2104628273
 
8.2%
60.4249656361
 
6.8%
40.9736166757
 
6.4%
57.9670211453
 
5.9%
43.0210815551
 
5.7%
36.891248738
 
4.3%
Other values (21)183
20.5%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
23.941682283
 
0.3%
25.615481581
 
0.1%
26.2359362
 
0.2%
27.458951662
 
0.2%
28.399195093
 
0.3%
29.574308455
 
0.6%
31.9557921419
2.1%
34.4330405122
2.5%
ValueCountFrequency (%)
1001
 
0.1%
75.562700415
 
0.6%
73.229130152
 
0.2%
71.772800376
 
0.7%
70.174606754
 
0.4%
69.389746553
 
0.3%
67.9661507414
1.6%
66.430056388
 
0.9%
64.6821354227
3.0%
62.8069736424
2.7%

uniformity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.28669015
Minimum0
Maximum100
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:58.217560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.093843
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.42395094
Coefficient of variation (CV)0.245643159
Kurtosis3.06804578
Mean91.28669015
Median Absolute Deviation (MAD)0
Skewness-2.220169909
Sum81427.72761
Variance502.8335759
MonotonicityNot monotonic
2022-02-19T11:11:58.305870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
100774
86.8%
37.09384374
 
8.3%
32.8895078520
 
2.2%
29.3084747618
 
2.0%
23.941682283
 
0.3%
02
 
0.2%
25.615481581
 
0.1%
ValueCountFrequency (%)
02
 
0.2%
23.941682283
 
0.3%
25.615481581
 
0.1%
29.3084747618
 
2.0%
32.8895078520
 
2.2%
37.09384374
 
8.3%
100774
86.8%
ValueCountFrequency (%)
100774
86.8%
37.09384374
 
8.3%
32.8895078520
 
2.2%
29.3084747618
 
2.0%
25.615481581
 
0.1%
23.941682283
 
0.3%
02
 
0.2%

clean_cup
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.35743063
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:58.398845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.09708327
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.8287358
Coefficient of variation (CV)0.1995469321
Kurtosis7.563718217
Mean94.35743063
Median Absolute Deviation (MAD)0
Skewness-3.068448336
Sum84166.82813
Variance354.521292
MonotonicityNot monotonic
2022-02-19T11:11:58.495002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
100818
91.7%
35.0970832739
 
4.4%
31.100742289
 
1.0%
32.40739748
 
0.9%
28.877410946
 
0.7%
27.013502174
 
0.4%
24.873097063
 
0.3%
29.825393252
 
0.2%
22.673824941
 
0.1%
01
 
0.1%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
22.673824941
 
0.1%
24.873097063
 
0.3%
27.013502174
 
0.4%
28.877410946
 
0.7%
29.825393252
 
0.2%
31.100742289
 
1.0%
32.40739748
 
0.9%
35.0970832739
4.4%
ValueCountFrequency (%)
100818
91.7%
35.0970832739
 
4.4%
32.40739748
 
0.9%
31.100742289
 
1.0%
29.825393252
 
0.2%
28.877410946
 
0.7%
27.013502174
 
0.4%
24.873097063
 
0.3%
22.673824941
 
0.1%
20.613640841
 
0.1%

sweetness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.14493988
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:58.594562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.40404857
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.11844474
Coefficient of variation (CV)0.2030745865
Kurtosis7.080583488
Mean94.14493988
Median Absolute Deviation (MAD)0
Skewness-2.989670013
Sum83977.28637
Variance365.5149293
MonotonicityNot monotonic
2022-02-19T11:11:58.690384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
100815
91.4%
35.4040485738
 
4.3%
31.955792149
 
1.0%
33.146772716
 
0.7%
28.04794385
 
0.6%
29.945949534
 
0.4%
25.615481583
 
0.3%
21.803334132
 
0.2%
30.610253452
 
0.2%
31.006537752
 
0.2%
Other values (5)6
 
0.7%
ValueCountFrequency (%)
01
 
0.1%
21.803334132
 
0.2%
23.941682281
 
0.1%
25.615481583
0.3%
26.770869851
 
0.1%
28.04794385
0.6%
29.169396882
 
0.2%
29.945949534
0.4%
30.610253452
 
0.2%
31.006537752
 
0.2%
ValueCountFrequency (%)
100815
91.4%
35.4040485738
 
4.3%
33.146772716
 
0.7%
32.688200091
 
0.1%
31.955792149
 
1.0%
31.006537752
 
0.2%
30.610253452
 
0.2%
29.945949534
 
0.4%
29.169396882
 
0.2%
28.04794385
 
0.6%

cupper_points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct37
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.01266618
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:58.801627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.90891031
Q143.3514376
median50.44655424
Q357.01416981
95-th percentile66.54771646
Maximum100
Range100
Interquartile range (IQR)13.66273221

Descriptive statistics

Standard deviation9.89330587
Coefficient of variation (CV)0.1978160059
Kurtosis1.605191849
Mean50.01266618
Median Absolute Deviation (MAD)6.567615576
Skewness0.1069872293
Sum44611.29823
Variance97.87750103
MonotonicityNot monotonic
2022-02-19T11:11:58.921298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
50.44655424105
11.8%
45.7537255985
 
9.5%
52.9967056881
 
9.1%
48.0247715673
 
8.2%
43.351437666
 
7.4%
55.1456825764
 
7.2%
58.9426681453
 
5.9%
57.0141698148
 
5.4%
41.284132545
 
5.0%
38.2330330640
 
4.5%
Other values (27)232
26.0%
ValueCountFrequency (%)
01
 
0.1%
20.613640841
 
0.1%
23.941682283
 
0.3%
25.615481581
 
0.1%
26.770869853
 
0.3%
28.399195095
 
0.6%
29.825393255
 
0.6%
30.610253453
 
0.3%
32.261178815
1.7%
33.9089103111
1.2%
ValueCountFrequency (%)
1002
 
0.2%
77.326175051
 
0.1%
76.058317721
 
0.1%
75.126902941
 
0.1%
73.7640642
 
0.2%
72.13928056
0.7%
70.174606756
0.7%
68.993462255
0.6%
68.203987384
0.4%
67.73882123
0.3%

total_cup_points
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct157
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.99488243
Minimum0
Maximum100
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-02-19T11:11:59.060528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.17684993
Q143.40287947
median50
Q356.63139403
95-th percentile65.80954703
Maximum100
Range100
Interquartile range (IQR)13.22851456

Descriptive statistics

Standard deviation9.830634988
Coefficient of variation (CV)0.1966328254
Kurtosis1.090328273
Mean49.99488243
Median Absolute Deviation (MAD)6.597120531
Skewness-0.001134807223
Sum44595.43513
Variance96.64138426
MonotonicityNot monotonic
2022-02-19T11:11:59.191302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.836128725
 
2.8%
5022
 
2.5%
50.5955722522
 
2.5%
53.8912099822
 
2.5%
45.7984164821
 
2.4%
47.5524835621
 
2.4%
46.7609183219
 
2.1%
52.3637740619
 
2.1%
51.8372725719
 
2.1%
53.2964058518
 
2.0%
Other values (147)684
76.7%
ValueCountFrequency (%)
01
0.1%
20.613640841
0.1%
22.673824941
0.1%
23.941682281
0.1%
25.262693852
0.2%
26.2359361
0.1%
26.770869851
0.1%
27.242328341
0.1%
27.664707331
0.1%
28.04794381
0.1%
ValueCountFrequency (%)
1001
 
0.1%
79.386359161
 
0.1%
77.326175051
 
0.1%
76.058317721
 
0.1%
75.126902941
 
0.1%
74.384518421
 
0.1%
73.7640641
 
0.1%
73.229130151
 
0.1%
72.757671661
 
0.1%
71.95205623
0.3%

Interactions

2022-02-19T11:11:47.310930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:04.490657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:06.827975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:09.249412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:11.520105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:14.109489image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:16.334038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:18.726701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:21.326518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:23.854269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:26.335574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:28.601161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:30.803515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:33.007321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:35.629527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:37.862813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-19T11:11:40.085844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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Correlations

2022-02-19T11:11:59.323936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-19T11:11:59.556718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-19T11:11:59.769234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-19T11:11:59.967154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-19T11:12:00.150848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-19T11:11:49.725098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-19T11:11:50.680910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-19T11:11:51.032364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-19T11:11:51.355842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

speciesownercountryof_originfarm_namelot_numbermillico_numbercompanyaltituderegionproducernumberof_bagsbag_weightin_country_partnerharvest_yeargrading_dateowner1varietyprocessing_methodmoisturecategory_one_defectsquakerscolorcategory_two_defectsexpirationunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_metersaromaflavoraftertasteaciditybodybalanceuniformityclean_cupsweetnesscupper_pointstotal_cup_points
0Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcNaNWashed / Wet0.1200.0Green0April 3rd, 2016m1950.02200.02075.077.326175100.000000100.000000100.00000077.32617571.772800100.000000100.0100.00000075.126903100.000000
1Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet0.1200.0Green1April 3rd, 2016m1950.02200.02075.0100.00000076.05831874.38451879.38635975.12690371.772800100.000000100.0100.00000072.13928079.386359
2Arabicagrounds for health adminGuatemalasan marcos barrancas "san cristobal cuchNaNNaNNaNNaN1600 - 1800 mNaNNaN51Specialty Coffee AssociationNaNMay 31st, 2010Grounds for Health AdminBourbonNaN0.0000.0NaN0May 31st, 2011m1600.01800.01700.072.54104872.54104872.98649872.13928073.22913071.772800100.000000100.0100.00000077.32617577.326175
3Arabicayidnekachew dabessaEthiopiayidnekachew dabessa coffee plantationNaNwolensuNaNyidnekachew debessa coffee plantation1800-2200oromiaYidnekachew Dabessa Coffee Plantation32060 kgMETAD Agricultural Development plc2014March 26th, 2015Yidnekachew DabessaNaNNatural / Dry0.1100.0Green2March 25th, 2016m1800.02200.02000.067.31180073.76406472.98649872.13928077.32617569.389747100.000000100.0100.00000073.76406476.058318
4Arabicametad plcEthiopiametad plcNaNmetad plc2014/2015metad agricultural developmet plc1950-2200guji-hambelaMETAD PLC30060 kgMETAD Agricultural Development plc2014April 4th, 2015metad plcOtherWashed / Wet0.1200.0Green2April 3rd, 2016m1950.02200.02075.069.08952572.54104871.60080574.73730675.12690370.174607100.000000100.0100.00000072.13928075.126903
5Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN0.0300.0NaN0September 2nd, 2011m1570.01700.01635.069.08952569.93656674.38451872.13928073.22913073.229130100.000000100.035.40404976.05831874.384518
6Arabicaethiopia commodity exchangeEthiopiaaolmeNaNc.p.w.e010/0338NaN1570-1700oromiyaBazen Agricultural & Industrial Dev't Plc30060 kgEthiopia Commodity ExchangeMarch 2010September 2nd, 2010Ethiopia Commodity ExchangeNaNNaN0.0300.0NaN0September 2nd, 2011m1570.01700.01635.077.32617576.05831877.32617572.13928073.22913071.77280037.093843100.035.40404973.76406473.764064
7Arabicadiamond enterprise plcEthiopiatulla coffee farmNaNtulla coffee farm2014/15diamond enterprise plc1795-1850snnp/kaffa zone,gimboweredaDiamond Enterprise Plc5060 kgMETAD Agricultural Development plc2014March 30th, 2015Diamond Enterprise PlcOtherNatural / Dry0.1000.0Green4March 29th, 2016m1795.01850.01822.565.82315073.76406474.38451874.73730655.91180171.772800100.000000100.0100.00000070.17460773.229130
8Arabicamohammed laloEthiopiafahem coffee plantationNaNNaNNaNfahem coffee plantation1855-1955oromiaFahem Coffee Plantation30060 kgMETAD Agricultural Development plc2014March 27th, 2015Mohammed LaloNaNNatural / Dry0.1000.0NaN1March 26th, 2016m1855.01955.01905.067.31180076.05831871.60080574.73730658.69549067.966151100.000000100.0100.00000072.13928072.757672
9Arabicacqi q coffee sample representativeUnited Statesel filoNaNNaNunknowncoffee quality institutemeters above sea level: 1.872antioquiaAlfredo De Jesús López Pérez101 kgAlmacafé2014March 13th, 2015CQI Q Coffee Sample RepresentativeOtherWashed / Wet0.0000.0NaN0March 12th, 2016m1872.01872.01872.069.08952570.97417970.17460770.17460768.45351067.966151100.000000100.0100.00000070.17460771.952056

Last rows

speciesownercountryof_originfarm_namelot_numbermillico_numbercompanyaltituderegionproducernumberof_bagsbag_weightin_country_partnerharvest_yeargrading_dateowner1varietyprocessing_methodmoisturecategory_one_defectsquakerscolorcategory_two_defectsexpirationunit_of_measurementaltitude_low_metersaltitude_high_metersaltitude_mean_metersaromaflavoraftertasteaciditybodybalanceuniformityclean_cupsweetnesscupper_pointstotal_cup_points
882Robustaandrew hetzelIndiasethuraman estateNaNNaN0000sethuraman estate1000mchikmagalurNishant Gurger3001 kgSpecialty Coffee Association2015April 30th, 2015Andrew HetzelNaNNaN0.0000.0Green0April 29th, 2016m1000.01000.01000.056.73472557.33580658.47385254.72897453.33947653.210463100.000000100.00000028.04794458.94266845.301533
883Robustaandrew hetzelIndiasethuraman estatesNaNsethuraman estatesNaNcafemakers, llc750mchikmagalurNishant Gurjer1402 kgSpecialty Coffee Association2013June 3rd, 2013Andrew HetzelNaNNatural / Dry0.1300.0Blue-Green0June 3rd, 2014m750.0750.0750.047.83094148.99718145.46849359.90469255.91180160.424966100.000000100.00000030.61025358.94266844.681783
884Robustakawacom uganda ltdUgandabushenyiNaNkawacom0kawacom uganda ltd1600westernKawacom uganda ltd160 kgUganda Coffee Development Authority2013June 27th, 2014Kawacom Uganda LTDNaNNaN0.1200.0Green1June 27th, 2015m1600.01600.01600.042.16567952.07203853.57034257.53645658.69549055.588295100.000000100.00000029.94595052.99670643.402879
885Robustamannya coffee projectUgandamannya coffee projectNaNmannya coffee project0mannya coffee project1200southernMannya coffee project660 kgUganda Coffee Development Authority2013June 27th, 2014Mannya coffee projectNaNNaN0.1200.0Green1June 27th, 2015m1200.01200.01200.056.73472546.28432348.20403951.93380055.91180153.210463100.000000100.00000029.16939750.44655441.918017
886Robustaandrew hetzelIndiasethuraman estatesNaNNaNNaNcafemakers750mchikmagalurNishant Gurjer1002 kgSpecialty Coffee Association2014May 19th, 2014Andrew HetzelNaNNaN0.0000.0Bluish-Green1May 19th, 2015m750.0750.0750.053.78866254.75952953.57034243.65732353.33947650.365319100.000000100.00000025.61548250.44655440.846745
887Robustaandrew hetzelIndiasethuraman estatesNaNsethuraman estatesNaNcafemakers, llc750mchikmagalurNishant Gurjer2502 kgSpecialty Coffee Association2013June 20th, 2013Andrew HetzelNaNNatural / Dry0.0000.0Green0June 20th, 2014m750.0750.0750.050.88048946.28432350.90766159.90469247.14497450.365319100.000000100.00000025.61548252.99670640.846745
888Robustaandrew hetzelUnited Statessethuraman estatesNaNsethuraman estatesNaNcafemakers, llc3000'chikmagalurSethuraman Estates1001 kgSpecialty Coffee Association2012February 29th, 2012Andrew HetzelArushaNatural / Dry0.0000.0Green0February 28th, 2013m3000.03000.03000.061.62526248.99718150.90766146.16744247.14497447.44053637.093843100.00000028.04794445.75372638.318360
889Robustaluis roblesEcuadorrobustasaLavado 1our own labNaNrobustasaNaNsan juan, playasCafé Robusta del Ecuador S.A.12 kgSpecialty Coffee Association2016January 19th, 2016Luis RoblesNaNNaN0.0000.0Blue-Green1January 18th, 2017mNaNNaNNaN56.73472552.07203848.20403951.9338000.00000060.424966100.000000100.00000029.94595058.94266836.787733
890Robustaluis roblesEcuadorrobustasaLavado 3own laboratoryNaNrobustasa40san juan, playasCafé Robusta del Ecuador S.A.12 kgSpecialty Coffee Association2016January 19th, 2016Luis RoblesNaNNaN0.0000.0Blue-Green0January 18th, 2017m40.040.040.047.83094154.75952960.79794957.53645620.6136410.000000100.000000100.00000032.68820072.13928035.186334
891Robustajames mooreUnited Statesfazenda cazengoNaNcafe cazengoNaNglobal opportunity fund795 meterskwanza norte province, angolaCafe Cazengo11 kgSpecialty Coffee Association2014December 23rd, 2014James MooreNaNNatural / Dry0.0000.0NaN6December 23rd, 2015m795.0795.0795.042.16567943.90740243.02108246.16744250.31117740.97361737.09384335.09708325.61548241.28413233.271008